Major Depressive Disorder is a common and disabling psychiatric illness, usually treated with a selective serotonin reuptake inhibitor antidepressant. Studies to date suggest that there are associations between antidepressant response and genes in both neurotransmitter regulatory pathways and antidepressant metabolism pathways. Based on these observations, this application is designed to identify genetic determinants for antidepressant response in a clinical sample of unprecedented size, treated with a single antidepressant, whose treatment response has been carefully ascertained. The ultimate goal is to elucidate genetic determinants of response to antidepressants as an important prerequisite to understanding the mechanism of antidepressant action and development of novel therapeutic agents for depression. Our specific hypothesis is that important phenotypes involving response to citalopram are in part mediated by detectable genetic factors. We propose a large-scale genetic association study on a collection of DNA's (about 1,400) obtained during the STAR*D (Sequenced Treatment Alternatives to Relieve Depression) protocol, a large multi-site treatment study involving about 4,000 persons with DSM-IV Major Depressive Disorder. We propose to: 1) genotype this sample for association between response phenotypes and variants in 20 antidepressant response candidate genes based on prior biological or genetic evidence, and 2) perform a whole genome association study between response phenotypes and about 100,000 gene-based DNA variants. Secondary specific aims are to: 1) sequence genes positively associated with response phenotypes identified using candidate gene or whole genome approaches to identify potential response-related alleles, and 2) develop refined phenotypes and novel hypotheses to test for association to treatment response outcomes. Power calculations suggest meaningful differences between phenotypic groupings can be detected. Detecting any association between DNA variations and antidepressant response could ultimately have a significant clinical impact if a genotype that accounts for a substantial portion of variance in response or tolerability of these medications is identified. These findings could provide steps toward our ability to define clinically useful genetic predictors of pharmacological treatment and apply them to patient populations.